Databases are commonly used to store data that underlies enterprise software systems. Through use of the software systems, users and automated processes access data stored in the database. As the number of users and automated processes grows, databases can become overwhelmed by the number of data requests and latency increases. To combat this latency, all or select portions of data stored in the database can be replicated to another database. The addition of a replicate database also typically includes the addition of a process that handles replication of data from a master database to the replicate database. While such master and replicate databases along with a data replication process can resolve database latency issues, other issues are introduced. One such issue arises when the master database is structurally changed, the replicate database may not be able to receive updates from the data replication process due to structural data storage mismatches. Resolving such issues to date have required manual modifications to both the master database and replicate database as well as changes to underlying data views, the data replication process, and other elements. As with many manual data processing tasks, these tasks are time consuming, expensive, and are prone to error.